Barcoded bulk QTL mapping reveals highly polygenic and epistatic architecture of complex traits in yeast

Abstract

Mapping the genetic basis of complex traits is critical to uncovering the biological mechanisms that underlie disease and other phenotypes. Genome-wide association studies (GWAS) in humans and quantitative trait locus (QTL) mapping in model organisms can now explain much of the observed heritability in many traits, allowing us to predict phenotype from genotype. However, constraints on power due to statistical confounders in large GWAS and smaller sample sizes in QTL studies still limit our ability to resolve numerous small-effect variants, map them to causal genes, identify pleiotropic effects across multiple traits, and infer non-additive interactions between loci (epistasis). Here, we introduce barcoded bulk quantitative trait locus (BB-QTL) mapping, which allows us to construct, genotype, and phenotype 100,000 offspring of a budding yeast cross, two orders of magnitude larger than the previous state of the art. We use this panel to map the genetic basis of eighteen complex traits, finding that the genetic architecture of these traits involves hundreds of small-effect loci densely spaced throughout the genome, many with widespread pleiotropic effects across multiple traits. Epistasis plays a central role, with thousands of interactions that provide insight into genetic networks. By dramatically increasing sample size, BB-QTL mapping demonstrates the potential of natural variants in high-powered QTL studies to reveal the highly polygenic, pleiotropic, and epistatic architecture of complex traits.

Large-scale changes of molecular network states explain complex traits

“…R 2 ), leaving about a quarter of the estimated heritability unexplained by effects at these QTL. Regarding potential mediators of the QTL effects, we noted that the locus on Chromosome XV (hilagQTL3) contained IRA2 , which is known to induce widespread changes of transcriptional and cellular traits in this yeast cross (Nguyen Ba et al, 2022; Smith and Kruglyak, 2008). Replacing IRA2 by the RM allele in the BY background indeed approximated the effect of the QTL (Fig.…”

“…Ira2 is a GTPase-activating protein (GAP) that negatively regulates Ras1/2, which are upstream regulators of the PKA signaling pathway. The RM allele of IRA2 is more active compared to the BY allele due to coding sequence differences (Nguyen Ba et al ., 2022; Smith and Kruglyak, 2008). Hence, the effects of the IRA2 locus on protein abundances likely result from genetic influence on PKA activity.…”

“…Specifically, the effect size of PTQTL1 (close to BUL2 ) was higher in strains carrying the RM allele at PTQTL3 (close to IRA2 , +1.10) than in strains carrying the BY allele (+0.21, p < 0.05 for the interaction term), whereas the effect size at PTQTL2 (close to MKT1/SAL1 ) was higher in strains carrying the BY allele (+1.14) than in those carrying the RM allele (+0.24, p < 0.05 for the interaction term) at PTQTL3. Potentially related interactions between major pleiotropic effect loci in determining cellular fitness traits have been described recently (Nguyen Ba et al ., 2022).…”

Barcoding populations ofPseudomonas fluorescensSBW25

“…When changes are measured over the course of hundreds of generations detailed understanding of eco-evolutionary dynamics can emerge (Levy et al 2015;Jasinska et al 2020;Acar et al 2020;Boyer et al 2021;Aggeli et al 2021), including insight into the connection between genotype and phenotype of even complex traits (Venkataram et al 2016;Kinsler et al 2020;Jagdish and Nguyen Ba 2022;Nguyen Ba et al 2022;Aggeli et al 2022;Ascensao et al 2022). Recent developments allowing regeneration of barcode diversity extend opportunity to observe evolutionary change over the course of thousands of generations (Ba et al 2019).…”

Gene-by-environment interactions are pervasive among natural genetic variants

“…Alternatively, quantitative trait locus (QTL) mapping uses genetic crosses between strains to create diverse progeny through recombination to calculate statistical signals that associate with environmental response (Bloom et al, 2013(Bloom et al, , 2019Ehrenreich et al, 2012;Smith and Kruglyak, 2008). However, it is generally impossible to identify the specific variants underlying a GWAS or QTL peak without laborious followup experiments, due to insufficient mapping resolution, though crosses with tens of thousands of recombinant genotypes can resolve some QTLs to single nucleotides (Nguyen Ba et al, 2022;Rockman, 2012;She and Jarosz, 2018).…”